style: move things :)

Handle_emg_data.py

@@ -12,7 +12,7 @@ class Data_container:
         self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8}
         self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8}
         self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8}
-        self.dict_list =    [self.data_dict_round1, 
+        self.dict_list =   [self.data_dict_round1, 
                             self.data_dict_round2, 
                             self.data_dict_round3, 
                             self.data_dict_round4
@@ -450,6 +450,8 @@ class CSV_handler:
         return df
 
 
+# HELP FUNCTIONS: ------------------------------------------------------------------------: 
+
 # Help: gets the str from emg nr
 def get_emg_str(emg_nr):
     return 'emg' + str(emg_nr)

Present_data.py

@@ -69,6 +69,7 @@ def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_d
 
     plt.show() 
 
+
 # DATA FUNCTIONS: --------------------------------------------------------------: 
 
 # The CSV_handler takes in data_type, but only for visuals. 
@@ -115,6 +116,7 @@ def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize):
     y = get_xory_from_df('y', df)
     return N, base.mfcc(y, samplesize, windowsize, stepsize)
 
+
 # CASE FUNTIONS ----------------------------------------------------------------: 
 
 # Takes in a df and compares the FFT and the wavelet denoising of the FFT
@@ -128,6 +130,9 @@ def compare_with_wavelet_filter(data_frame):
 
     plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
 
+# Loads three preset emg datasets, calculates mfcc for each and plots them.
+# Input: CSV_handler
+# Output: None --> Plot
 def compare_mfcc_3_plots(csv_handler:CSV_handler):
     df1, samplerate1 = get_data(csv_handler, 1, 'left', 1, 1)
     df2, samplerate2 = get_data(csv_handler, 1, 'left', 2, 1)
@@ -146,6 +151,7 @@ def compare_mfcc_3_plots(csv_handler:CSV_handler):
 
     plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3)
 
+
 # MAIN: ------------------------------------------------------------------------: 
 
 def main():

Signal_prep.py

@@ -2,14 +2,11 @@ import numpy as np
 from pandas.core.frame import DataFrame
 from scipy.fft import fft, fftfreq
 import pywt
-#from pyhton_speech_features.base import mfcc
 import sys
+import Handle_emg_data as Handler
 sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features')
 from python_speech_features.python_speech_features import *
 
-import Handle_emg_data as Handler
-
-
 
 # Takes in a df and outputs np arrays for x and y values
 def get_xory_from_df(x_or_y, df:DataFrame):
@@ -42,14 +39,6 @@ def wavelet_db4(df:DataFrame):
     N_trans =  np.array(range(int(np.floor((y_values.size + wavelet.dec_len - 1) / 2))))
     return N_trans, cA, cD 
 
-# Filters signal accordning to Stein's Unbiased Risk Estimate(SURE)
-'''
-def sure_threshold_filter(cA, cD):
-    cA_filt = pyyawt.theselect(cA, 'rigrsure')
-    cD_filt = cD 
-    return cA_filt, cD_filt
-'''
-
 # soft filtering of wavelet trans with the a 1/2 std filter 
 def soft_threshold_filter(cA, cD):
     cA_filt = pywt.threshold(cA, np.std(cA)/2)
@@ -69,16 +58,9 @@ def inverse_wavelet(df, cA_filt, cD_filt):
             old_len = len(get_xory_from_df('y', df))
     return y_new_values
 
+# NOT FINISHED
 def cepstrum(df:DataFrame):
     N = get_xory_from_df('x', df)
     y = get_xory_from_df('y', df)
-    
-    
     return None
 
-def mfcc(df:DataFrame, samplesize, windowsize, stepsize):
-    N = get_xory_from_df('x', df)
-    y = get_xory_from_df('y', df)
-    return N, base.mfcc(y, samplesize, windowsize, stepsize)
-    
-